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Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane.

Pang M, Guo S, Huang Q, Ishihara H, Hirata H - J Med Biol Eng (2015)

Bottom Line: The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally.The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments.It is also easier to calibrate and implement.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Engineering, Kagawa University, Takamatsu, 761-0396 Japan.

ABSTRACT

This paper presents a quantitative representation method for the upper-limb elbow joint angle using only electromyography (EMG) signals for continuous elbow joint voluntary flexion and extension in the sagittal plane. The dynamics relation between the musculotendon force exerted by the biceps brachii muscle and the elbow joint angle is developed for a modified musculoskeletal model. Based on the dynamics model, a quadratic-like quantitative relationship between EMG signals and the elbow joint angle is built using a Hill-type-based muscular model. Furthermore, a state switching model is designed to stabilize the transition of EMG signals between different muscle contraction motions during the whole movement. To evaluate the efficiency of the method, ten subjects performed continuous experiments during a 4-day period and five of them performed a subsequent consecutive stepping test. The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally. The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments. It is also easier to calibrate and implement.

No MeSH data available.


Related in: MedlinePlus

Prediction results of flexion and extension motion without holding motion. The proposed state switching method gives rise to distortion or time lag around the peak point of the motion
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Fig7: Prediction results of flexion and extension motion without holding motion. The proposed state switching method gives rise to distortion or time lag around the peak point of the motion

Mentions: Nevertheless, the proposed state switching model may give rise to distortion or time lag in some cases. In Fig. 7, the motion is forearm flexion and then extension, without a holding period during flexion and extension. There is a time lag between the flexion and extension in the prediction results. This is because the state changes from flexion to holding and then to extension. It takes some time (as long as the time lag) for the model to change state from holding to extension. This time lag depends on the decreasing rate of EMG signals (γ), the difference between peak muscle activation levels (aP), the threshold set for the holding state, and a range value (ar: 1–3 %) that is used to reduce the influence of the non-stationarity of EMG signals. The time lag can be defined as:24\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{lag} = \frac{{F_{p} - F_{t} (1 - a_{r} )}}{\gamma }$$\end{document}tlag=Fp-Ft(1-ar)γFig. 7


Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane.

Pang M, Guo S, Huang Q, Ishihara H, Hirata H - J Med Biol Eng (2015)

Prediction results of flexion and extension motion without holding motion. The proposed state switching method gives rise to distortion or time lag around the peak point of the motion
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4414936&req=5

Fig7: Prediction results of flexion and extension motion without holding motion. The proposed state switching method gives rise to distortion or time lag around the peak point of the motion
Mentions: Nevertheless, the proposed state switching model may give rise to distortion or time lag in some cases. In Fig. 7, the motion is forearm flexion and then extension, without a holding period during flexion and extension. There is a time lag between the flexion and extension in the prediction results. This is because the state changes from flexion to holding and then to extension. It takes some time (as long as the time lag) for the model to change state from holding to extension. This time lag depends on the decreasing rate of EMG signals (γ), the difference between peak muscle activation levels (aP), the threshold set for the holding state, and a range value (ar: 1–3 %) that is used to reduce the influence of the non-stationarity of EMG signals. The time lag can be defined as:24\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{lag} = \frac{{F_{p} - F_{t} (1 - a_{r} )}}{\gamma }$$\end{document}tlag=Fp-Ft(1-ar)γFig. 7

Bottom Line: The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally.The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments.It is also easier to calibrate and implement.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Engineering, Kagawa University, Takamatsu, 761-0396 Japan.

ABSTRACT

This paper presents a quantitative representation method for the upper-limb elbow joint angle using only electromyography (EMG) signals for continuous elbow joint voluntary flexion and extension in the sagittal plane. The dynamics relation between the musculotendon force exerted by the biceps brachii muscle and the elbow joint angle is developed for a modified musculoskeletal model. Based on the dynamics model, a quadratic-like quantitative relationship between EMG signals and the elbow joint angle is built using a Hill-type-based muscular model. Furthermore, a state switching model is designed to stabilize the transition of EMG signals between different muscle contraction motions during the whole movement. To evaluate the efficiency of the method, ten subjects performed continuous experiments during a 4-day period and five of them performed a subsequent consecutive stepping test. The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally. The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments. It is also easier to calibrate and implement.

No MeSH data available.


Related in: MedlinePlus